




"""simple docstring"""






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__="attention"						):
				_lowerCamelCase	:     Any       =       params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
				_lowerCamelCase	:     Tuple       =       params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
				_lowerCamelCase	:     List[Any]       =       params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
				_lowerCamelCase	:     List[Any]       =       params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
				return k, o, q, v







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__=False						):
				if split_mlp_wi:
								_lowerCamelCase	:     List[Any]       =       params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
								_lowerCamelCase	:     Optional[int]       =       params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
								_lowerCamelCase	:     Union[str, Any]       =       (wi_a, wi_a)
				else:
								_lowerCamelCase	:     List[str]       =       params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

				_lowerCamelCase	:     Optional[Any]       =       params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
				return wi, wo







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']







def 	_snake_case     (							lowercase__				,       *, lowercase__				,       lowercase__						):
				_lowerCamelCase	:     List[Any]       =       traverse_util.flatten_dict(variables['target']						)
				_lowerCamelCase	:     List[Any]       =       {'/'.join(UpperCamelCase__						): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				_lowerCamelCase	:     Tuple       =       'encoder/layers_0/mlp/wi_0/kernel' in old
				print('Split MLP:'				,       UpperCamelCase__						)

				_lowerCamelCase	:     List[Any]       =       collections.OrderedDict()

				# Shared embeddings.
				_lowerCamelCase	:     Optional[Any]       =       old['token_embedder/embedding']

				# Encoder.
				for i in range(UpperCamelCase__						):
								# Block i, layer 0 (Self Attention).
								_lowerCamelCase	:     Optional[Any]       =       tax_layer_norm_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'encoder'				,       'pre_attention_layer_norm'						)
								_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     Any       =       tax_attention_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'encoder'				,       'attention'						)
								_lowerCamelCase	:     Tuple       =       layer_norm
								_lowerCamelCase	:     Dict       =       k.T
								_lowerCamelCase	:     Optional[Any]       =       o.T
								_lowerCamelCase	:     int       =       q.T
								_lowerCamelCase	:     str       =       v.T

								# Block i, layer 1 (MLP).
								_lowerCamelCase	:     Tuple       =       tax_layer_norm_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'encoder'				,       'pre_mlp_layer_norm'						)
								_lowerCamelCase,					_lowerCamelCase	:     List[Any]       =       tax_mlp_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'encoder'				,       UpperCamelCase__						)
								_lowerCamelCase	:     Optional[int]       =       layer_norm
								if split_mlp_wi:
												_lowerCamelCase	:     str       =       wi[0].T
												_lowerCamelCase	:     Any       =       wi[1].T
								else:
												_lowerCamelCase	:     Tuple       =       wi.T
								_lowerCamelCase	:     Any       =       wo.T

				_lowerCamelCase	:     str       =       old[
				    'encoder/relpos_bias/rel_embedding'
				].T
				_lowerCamelCase	:     Tuple       =       old['encoder/encoder_norm/scale']

				if not is_encoder_only:
								# Decoder.
								for i in range(UpperCamelCase__						):
												# Block i, layer 0 (Self Attention).
												_lowerCamelCase	:     Tuple       =       tax_layer_norm_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'decoder'				,       'pre_self_attention_layer_norm'						)
												_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     List[Any]       =       tax_attention_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'decoder'				,       'self_attention'						)
												_lowerCamelCase	:     str       =       layer_norm
												_lowerCamelCase	:     List[Any]       =       k.T
												_lowerCamelCase	:     int       =       o.T
												_lowerCamelCase	:     List[Any]       =       q.T
												_lowerCamelCase	:     Optional[Any]       =       v.T

												# Block i, layer 1 (Cross Attention).
												_lowerCamelCase	:     List[Any]       =       tax_layer_norm_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'decoder'				,       'pre_cross_attention_layer_norm'						)
												_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase,					_lowerCamelCase	:     Dict       =       tax_attention_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'decoder'				,       'encoder_decoder_attention'						)
												_lowerCamelCase	:     Tuple       =       layer_norm
												_lowerCamelCase	:     str       =       k.T
												_lowerCamelCase	:     Optional[Any]       =       o.T
												_lowerCamelCase	:     Optional[int]       =       q.T
												_lowerCamelCase	:     Optional[Any]       =       v.T

												# Block i, layer 2 (MLP).
												_lowerCamelCase	:     Dict       =       tax_layer_norm_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'decoder'				,       'pre_mlp_layer_norm'						)
												_lowerCamelCase,					_lowerCamelCase	:     List[Any]       =       tax_mlp_lookup(UpperCamelCase__				,       UpperCamelCase__				,       'decoder'				,       UpperCamelCase__						)
												_lowerCamelCase	:     Optional[int]       =       layer_norm
												if split_mlp_wi:
																_lowerCamelCase	:     Tuple       =       wi[0].T
																_lowerCamelCase	:     Union[str, Any]       =       wi[1].T
												else:
																_lowerCamelCase	:     Optional[int]       =       wi.T
												_lowerCamelCase	:     List[str]       =       wo.T

								_lowerCamelCase	:     Union[str, Any]       =       old['decoder/decoder_norm/scale']
								_lowerCamelCase	:     List[str]       =       old[
								    'decoder/relpos_bias/rel_embedding'
								].T

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												_lowerCamelCase	:     Dict       =       old['decoder/logits_dense/kernel'].T

				return new







def 	_snake_case     (							lowercase__				,       lowercase__						):
				_lowerCamelCase	:     str       =       collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

				# Add what is missing.
				if "encoder.embed_tokens.weight" not in state_dict:
								_lowerCamelCase	:     str       =       state_dict['shared.weight']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												_lowerCamelCase	:     List[Any]       =       state_dict['shared.weight']

								if "lm_head.weight" not in state_dict:  # For old 1.0 models.
												print('Using shared word embeddings as lm_head.'						)
												_lowerCamelCase	:     str       =       state_dict['shared.weight']

				return state_dict







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Any       =       checkpoints.load_tax_checkpoint(UpperCamelCase__						)
				_lowerCamelCase	:     Optional[int]       =       convert_tax_to_pytorch(UpperCamelCase__				,       num_layers=config.num_layers				,       is_encoder_only=UpperCamelCase__						)
				_lowerCamelCase	:     int       =       make_state_dict(UpperCamelCase__				,       UpperCamelCase__						)
				model.load_state_dict(UpperCamelCase__				,       strict=UpperCamelCase__						)







def 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__ = False						):
				_lowerCamelCase	:     Tuple       =       TaConfig.from_json_file(UpperCamelCase__						)
				print(f'''Building PyTorch model from configuration: {config}'''						)
				# Non-v1.1 checkpoints could also use T5Model, but this works for all.
				# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
				if is_encoder_only:
								_lowerCamelCase	:     int       =       TaEncoderModel(UpperCamelCase__						)
				else:
								_lowerCamelCase	:     Optional[int]       =       TaForConditionalGeneration(UpperCamelCase__						)

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__						)

				# Save pytorch-model
				print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
				model.save_pretrained(UpperCamelCase__						)

				# Verify that we can load the checkpoint.
				model.from_pretrained(UpperCamelCase__						)
				print('Done'						)


if __name__ == "__main__":
			lowercase__			=		argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
			# Required parameters
			parser.add_argument(
			    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
			)
			parser.add_argument(
			    """--config_file""",
			    default=None,
			    type=str,
			    required=True,
			    help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
			)
			parser.add_argument(
			    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			parser.add_argument(
			    """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
			)
			lowercase__			=		parser.parse_args()
			convert_tax_checkpoint_to_pytorch(
			    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
			)
'''simple docstring'''




from manim import *




class        a__ (  UpperCAmelCase__					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    Rectangle(height=0.5    ,							width=0.5       )
         __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0       )

         __lowerCamelCase            =    Rectangle(height=0.25    ,							width=0.25       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''CPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         cpu.move_to([-2.5, -0.5, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(4       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''GPU'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         gpu.move_to([-1, -1, 0]       )
         self.add(a       )

         __lowerCamelCase            =    [mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )

         __lowerCamelCase            =    Text('''Model'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         model.move_to([3, -1.0, 0]       )
         self.add(a       )

         __lowerCamelCase            =    []
         __lowerCamelCase            =    []

         for i, rect in enumerate(a       ):
             __lowerCamelCase            =    fill.copy().set_fill(a    ,							opacity=0.8       )
             target.move_to(a       )
             model_arr.append(a       )

             __lowerCamelCase            =    Rectangle(height=0.46    ,							width=0.46       ).set_stroke(width=0.0       ).set_fill(a    ,							opacity=0.8       )
             cpu_target.move_to(cpu_left_col_base[i]       )
             model_cpu_arr.append(a       )

         self.add(*a    ,							*a       )

         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    [meta_mem.copy() for i in range(6       )]
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(*a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    VGroup(a    ,							a       ).arrange(a    ,							buff=0       )
         __lowerCamelCase            =    Text('''Disk'''    ,							font_size=24       )
         __lowerCamelCase            =    Group(a    ,							a       ).arrange(a    ,							buff=0.5    ,							aligned_edge=a       )
         disk.move_to([-4, -1.25, 0]       )
         self.add(a    ,							a       )

         __lowerCamelCase            =    Square(side_length=2.2       )
         key.move_to([-5, 2, 0]       )

         __lowerCamelCase            =    MarkupText(
             f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""    ,							font_size=18    ,							)

         key_text.move_to([-5, 2.4, 0]       )

         self.add(a    ,							a       )

         __lowerCamelCase            =    MarkupText(
             f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""    ,							font_size=18    ,							)

         blue_text.next_to(a    ,							DOWN * 2.4    ,							aligned_edge=key_text.get_left()       )
         self.add(a       )

         __lowerCamelCase            =    MarkupText(
             f"""Now watch as an input is passed through the model\nand how the memory is utilized and handled."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a       )       )

         __lowerCamelCase            =    Square(0.3       )
         input.set_fill(a    ,							opacity=1.0       )
         input.set_stroke(width=0.0       )
         input.next_to(model_base[0]    ,							a    ,							buff=0.5       )

         self.play(Write(a       )       )

         input.generate_target()
         input.target.next_to(model_arr[0]    ,							direction=a    ,							buff=0.02       )
         self.play(MoveToTarget(a       )       )

         self.play(FadeOut(a       )       )

         __lowerCamelCase            =    Arrow(start=a    ,							end=a    ,							color=a    ,							buff=0.5       )
         a.next_to(model_arr[0].get_left()    ,							a    ,							buff=0.2       )

         model_cpu_arr[0].generate_target()
         model_cpu_arr[0].target.move_to(gpu_rect[0]       )

         __lowerCamelCase            =    MarkupText(
             f"""As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."""    ,							font_size=24    ,							)
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )       )

         __lowerCamelCase            =    {'''run_time''': 1, '''fade_in''': True, '''fade_out''': True, '''buff''': 0.02}

         self.play(
             Write(a       )    ,							Circumscribe(model_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_cpu_arr[0]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)
         self.play(MoveToTarget(model_cpu_arr[0]       )       )

         __lowerCamelCase            =    a.copy()
         for i in range(6       ):
             a_c.next_to(model_arr[i].get_right() + 0.02    ,							a    ,							buff=0.2       )

             input.generate_target()
             input.target.move_to(model_arr[i].get_right() + 0.02       )

             __lowerCamelCase            =    AnimationGroup(
                 FadeOut(a    ,							run_time=0.5       )    ,							MoveToTarget(a    ,							run_time=0.5       )    ,							FadeIn(a    ,							run_time=0.5       )    ,							lag_ratio=0.2       )

             self.play(a       )

             model_cpu_arr[i].generate_target()
             model_cpu_arr[i].target.move_to(cpu_left_col_base[i]       )

             if i < 5:
                 model_cpu_arr[i + 1].generate_target()
                 model_cpu_arr[i + 1].target.move_to(gpu_rect[0]       )
                 if i >= 1:
                     __lowerCamelCase            =    0.7

                 self.play(
                     Circumscribe(model_arr[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i]    ,							**a       )    ,							Circumscribe(cpu_left_col_base[i + 1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							Circumscribe(model_arr[i + 1]    ,							color=a    ,							**a       )    ,							)
                 if i < 1:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]       )    ,							MoveToTarget(model_cpu_arr[i + 1]       )    ,							)
                 else:
                     self.play(
                         MoveToTarget(model_cpu_arr[i]    ,							run_time=0.7       )    ,							MoveToTarget(model_cpu_arr[i + 1]    ,							run_time=0.7       )    ,							)
             else:
                 model_cpu_arr[i].generate_target()
                 model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]       )
                 input.generate_target()
                 input.target.next_to(model_arr[-1].get_right()    ,							RIGHT + 0.02    ,							buff=0.2       )

                 self.play(
                     Circumscribe(model_arr[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(cpu_left_col_base[-1]    ,							color=a    ,							**a       )    ,							Circumscribe(gpu_rect[0]    ,							color=a    ,							**a       )    ,							)

                 self.play(MoveToTarget(model_cpu_arr[i]       )       )

             __lowerCamelCase            =    a_c
             __lowerCamelCase            =    a_c.copy()

         input.generate_target()
         input.target.next_to(model_base[-1]    ,							RIGHT + 0.02    ,							buff=0.5       )
         self.play(
             FadeOut(a       )    ,							FadeOut(a    ,							run_time=0.5       )    ,							)

         __lowerCamelCase            =    MarkupText(f"""Inference on a model too large for GPU memory\nis successfully completed."""    ,							font_size=24       )
         step_a.move_to([2, 2, 0]       )

         self.play(Write(a    ,							run_time=3       )    ,							MoveToTarget(a       )       )

         self.wait()
